#timeseries #similarity #aeon #elasticdistance #dtw #distances
Корреляция Пирсона тихо плачет в сторонке
https://www.youtube.com/watch?v=O5cnKAUBKkg
Корреляция Пирсона тихо плачет в сторонке
https://www.youtube.com/watch?v=O5cnKAUBKkg
YouTube
Lets do the time warp again: time series machine learning with distance functions | PDAMS 2023
Many algorithms for machine learning from time series are based on measuring the distance or similarity between series. The most popular distance measure is dynamic time warping, which attempts to optimally realign two series to compensate for offest. There…
#distances #trading #knn
"Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data. This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance. Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity. Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets.
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time"."
https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/
"Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data. This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance. Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity. Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets.
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time"."
https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/
TradingView
Machine Learning: Lorentzian Classification — Indicator by jdehorty — TradingView
█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to…
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to…